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Found 6 papers, 1 papers with code
TaSIL: Taylor Series Imitation Learning
We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control.
Adversarial Tradeoffs in Robust State Estimation
We provide an algorithm to find this perturbation given data realizations, and develop upper and lower bounds on the adversarial state estimation error in terms of the standard (non-adversarial) estimation error and the spectral properties of the resulting observer.
Adversarially Robust Stability Certificates can be Sample-Efficient
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems.
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control
Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms.
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation
In these special cases, we demonstrate that the severity of the tradeoff depends in an interpretable manner upon system-theoretic properties such as the spectrum of the controllability gramian, the spectrum of the observability gramian, and the stability of the system.
